Machine learning-based compression of quantum many body physics: PCA and autoencoder representation...
Machine learning-based compression of quantum many body physics: PCA and autoencoder representation of the vertex function
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Author / Creator
Publisher
Bristol: IOP Publishing
Journal title
Language
English
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Publisher
Bristol: IOP Publishing
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Scope and Contents
Contents
Theoretical approaches to quantum many-body physics require developing compact representations of the complexity of generic quantum states. This paper explores an interpretable data-driven approach utilizing principal component analysis (PCA) and
autoencoder
neural networks to compress the two-particle vertex, a key element in Feynman diagram...
Alternative Titles
Full title
Machine learning-based compression of quantum many body physics: PCA and autoencoder representation of the vertex function
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Record Identifier
TN_cdi_crossref_primary_10_1088_2632_2153_ad9f20
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_crossref_primary_10_1088_2632_2153_ad9f20
Other Identifiers
ISSN
2632-2153
E-ISSN
2632-2153
DOI
10.1088/2632-2153/ad9f20
